{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Citations with LlamaParse Layout Agent Mode\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/parsing_modes/demo_layout_agent_mode_visual_citations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "Status:\n",
    "| Last Executed | Version | State      |\n",
    "|---------------|---------|------------|\n",
    "| Prior to Feb-2025   | N/A  | Deprecated |\n",
    "\n",
    "This cookbook will show you how to leverage LlamaParse's new Layout Agent mode to build a query engine that provides visually grounded citations. But first—what exactly is Layout Agent mode?\n",
    "\n",
    "## Layout Agent Mode\n",
    "\n",
    "This new mode will parse pages in a layout-aware way. Instead of treating a page as plain text, Layout Agent sees the document structure and breaks it into visual blocks such as text, lists, figures, and tables. Each block is then parsed using the most suitable AI model.\n",
    "\n",
    "![Layout parsing moe](layout_agent_moe.png)\n",
    "\n",
    "It is harder to parse a chart than just transcribing text. That's why Layout Agent mode will use bigger/more expensive models for hard tasks and lighter/more efficient models for more naive and simple tasks.\n",
    "\n",
    "- 🟢 Easy to parse → Uses lightweight, general models\n",
    "- 🟡 Moderate difficulty → Uses specialized, efficient models\n",
    "- 🔴 Hard to parse → Uses larger, more capable models\n",
    "\n",
    "This Mixture of Experts approach ensures both efficiency and accuracy, allocating resources where they’re needed most.\n",
    "\n",
    "### Visually Grounded Parsing\n",
    "![Layout Agent explainer](layout_agent_parse_explainer.png)\n",
    "\n",
    "One of the most powerful features of Layout Agent mode is that every extracted Markdown block includes a bounding box, a visual reference that shows exactly where the information came from on the page.\n",
    "\n",
    "With visual references, you can build applications that preserve document structure and provide users with trustworthy, traceable visual citations. We will now leverage this feature to build our query engine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query Engine with Visual Citations\n",
    "\n",
    "![query engine with visual citations illustrations](layout_agent_citation_engine.png)\n",
    "\n",
    "Now, let's build a [query engine](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/) that provides **visually grounded answers** from a financial report by the International Monetary Fund (IMF).\n",
    "\n",
    "By the end of this cookbook, you'll be able to:\n",
    "- ✅ Ask questions about the document\n",
    "- ✅ Receive answers with citations\n",
    "- ✅ See exactly where the information came from"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, [get an api key](https://docs.cloud.llamaindex.ai/llamaparse/getting_started/get_an_api_key) and set it as an environment variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-xxxxxx\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now install the required packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-cloud-services llama-index-core nest_asyncio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our API key and libraries available, let's setup our parser and allow nested async loops:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_cloud_services import LlamaParse\n",
    "\n",
    "parser = LlamaParse(parse_mode=\"parse_page_with_layout_agent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will parse the PDF file and return the JSON output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parsed_pdf = parser.get_json_result(\"imf-report.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our report is now parsed and in JSON format, let's convert this into a list of Documents. We also save the bbox along with the text and page index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import Document\n",
    "\n",
    "pages = parsed_pdf[0][\"pages\"]\n",
    "documents = []\n",
    "\n",
    "for i, page in enumerate(pages):\n",
    "    # loop through items of the page\n",
    "    for item in page[\"items\"]:\n",
    "        document = Document(\n",
    "            text=item[\"md\"], extra_info={\"bbox\": item[\"bBox\"], \"page\": i}\n",
    "        )\n",
    "        documents.append(document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go a step further, and query this report using an LLM! For this, you will need an OpenAI API key (LlamaIndex supports many LLMs, we're just picking a popular one). Get an OpenAI API key and add it to your environment variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-xxxxxx\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also need to encode the documents into an index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index-llms-openai llama-index-embeddings-openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import VectorStoreIndex\n",
    "\n",
    "# create an index from the parsed documents\n",
    "index = VectorStoreIndex.from_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are almost done, let's query this index using `CitationQueryEngine`. This engine will give answers and also returns the cited nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.query_engine import CitationQueryEngine\n",
    "\n",
    "query_engine = CitationQueryEngine.from_args(index, similarity_top_k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's query our document!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The largest 5 users of GRA credit as of October 31, 2024, are Argentina, Ecuador, Ukraine, Egypt, Arab Republic of, and Jordan [2].\n"
     ]
    }
   ],
   "source": [
    "response = query_engine.query(\n",
    "    \"What are the largest 5 users of GRA credit as of 31 october 2024?\"\n",
    ")\n",
    "print(response.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the LLM is citing the source number 2, let's first extract it from the source nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# llm citations are 1 indexed (source 2 -> index 1)\n",
    "citation = response.source_nodes[1]\n",
    "\n",
    "# get the visual bbox and the page number of the citation\n",
    "bbox = citation.node.metadata[\"bbox\"]\n",
    "page_index = citation.node.metadata[\"page\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's time to visualize this citation! We will use pymupdf to take a screenshot of the page and display the citation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pymupdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fitz\n",
    "from PIL import Image\n",
    "\n",
    "# load page of the pdf\n",
    "pdf = fitz.open(\"imf-report.pdf\")\n",
    "page = pdf[page_index]\n",
    "\n",
    "# take screenshot of the page\n",
    "pix = page.get_pixmap(dpi=200)\n",
    "image = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
    "\n",
    "# resize the page to align the bbox coordinates\n",
    "parsed_page = pages[page_index]\n",
    "image = image.resize((parsed_page[\"width\"], parsed_page[\"height\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can visually see the citation of the LLM. In this case, it cited the table that contained the answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=462x192>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.crop((bbox[\"x\"], bbox[\"y\"], (bbox[\"x\"] + bbox[\"w\"]), (bbox[\"y\"] + bbox[\"h\"])))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
